Specifications for MSE Trials for North Atlantic Swordfish
Introduction
The North Atlantic swordfish fishery, under the management of the International Commission for the Conservation of Atlantic Tuna (ICCAT), is undergoing a management strategy evaluation (MSE) process.
ICCAT describes MSE as:
a collaborative process between Scientists and decision-makers that involves using computer simulation to compare the relative ability to achieve a set of management objectives using alternative Management Strategies, defined as different combinations of data collection schemes, methods of analysis, harvest control rules and subsequent processes leading to management actions.
There are three main components in an MSE process:
- Operating models (OMs): a collection of mathematical/statistical models that describe alternative hypotheses of the historical fishery dynamics and specifications for simulating the collection of data and implementation of management measures in the future;
- Candidate management procedures (CMPs): a set of proposed algorithms that generate management recommendations from fishery data, and will be evaluated in the MSE;
- Performance metrics (PMs): statistics use the quantitatively evaluate the CMPs against specified management objectives.
The operating models, candidate management procedures, and performance metrics are developed as a collaborative effort between scientists, decision-makers, and other stakeholders in the fishery.
About this document
This document describes the specifications for the OMs, CMPs, and PMs that have been proposed and developed for the North Atlantic swordfish (hereafter swordfish) fishery.
It is a living document and will be continued to be updated so that it reflects the current state of the swordfish MSE process.
Members of the Swordfish Species Group (hereafter the Group) are encouraged to provide feedback, comments, or edits to any part of this document.
The document is written using the Markdown format and can edited in any text editor. The source document is available on the ICCAT/nswo-mse GitHub repository.
Group members can make edits to the document either directly in the online repository or by cloning the repository and submitting pull requests with their edits. Alternatively, they can email questions or comments to Adrian. The former approach has the advantage that all comments, questions, and edits are immediately visible to all members of the Group.
Group members can also use the Discussions feature on the Github repository to post questions, comments, or points for discussion related to any aspect of this document or the MSE process in general.
This document is available at the North Atlantic Swordfish MSE homepage.
MSE Framework
The SWOMSE R package has been developed to conduct the MSE for the Atlantic swordfish fishery. All code used for the MSE is open-source and reproducible. Group members can install the package and reproduce the analysis on their own machines.
The SWOMSE package used for the swordfish MSE uses the openMSE framework. The openMSE package is automatically installed when SWOMSE is installed, and all openMSE functions are available to the user when the SWOMSE package is loaded.
openMSE is an R package that has been developed for conducting fast, flexible, and transparent, MSE for a wide range of fisheries. openMSE is an umbrella package that includes the MSEtool, SAMtool and DLMtool packages. A non-technical description of openMSE and its key features is available on the openMSE website.
The operating model in openMSE, including assumptions and equations, is described in detail in Carruthers & Hordyk (2018).
The SWOMSE User Manual includes instructions for installing the SWOMSE package and running the MSE analyses.
Stock Assessment
Previously, the swordfish operating models were based on the 2017 stock assessment (Anon., 2017) using Stock Synthesis 3 (SS3, Methot & Wetzel, 2013).
A new stock assessment was conducted in 2022. The swordfish operating models have been updated based on this new assessment.
The report for the 2022 stock assessment is published in SCRS/2022/012. Details of the 2022 assessment are published in SCRS/2022/124. The data used in the 2022 assessment, and the structure and assumptions of the assessment model are summarized in the sub-sections below.
Data
The assessment used landings data from 8 longline fleets (7 national fleets and ‘Other’ representing all catches not accounted for in the national fleets) (Table 3.1 and Figure 3.1).
There were 6 indices of abundance derived from catch-per-unit-effort (CPUE) data from the national fleets (Table 3.1 and Figure 3.1). Data from the Japan longline fleet was split into two phases: Early (1950 - 1993), and Late (1994 - 2020) due to changes in fishing practices in these periods. Similarly, the Chinese-Taipei longline fleet was split into two phases: Early (1968 - 1989) and Late (1997 - 2020). Five additional survey indices were developed from the age-specific (ages estimated from length data) CPUE from the Spain longline fleet (Table 3.1 and Figure 3.1).
Length composition data from 7 national longline fleets (Spain, U.S., Canada, Japan (Early, Late), Portugal, Chinese-Tapai (Early, Late), and Morocco) and the Canada/USA harpoon fleet (Figure 3.1). Mean weight data from two fleets (U.S. and Canada) were also used (Figure 3.1).
The catchability coefficient (q) for the CPUE indices for Canada, Morocco, and the Age-1, Age-2, and Age-4 Spain age-specific survey indices, was made a function of the Atlantic Multidecadal Oscillation (AMO) (see SCRS/2022/124 for details).
The effective sample size (ESS) for the length composition data was established by adjusting ESS until unity was reached between modeled ESS and the Francis suggested sample size (See Anon., 2017 for details).
Table 3.1: Summary table of the fishing fleets and indices of abundances included the 2022 stock assessment of North Atlantic swordfish.
|
Name
|
Code
|
|
EU Spain longline (LL)
|
SPN 1
|
|
USA LL
|
US 2
|
|
Canada LL
|
CAN 3
|
|
Japan LL Early
|
JPN ERLY 4
|
|
Japan LL Late
|
JPN LATE 5
|
|
EU Portugal LL
|
PORT 6
|
|
Chinese Taipai LL Early
|
CHT EARLY 7
|
|
Chinese Taipai LL Late
|
CHT LATE 8
|
|
Morocco LL
|
MOR 9
|
|
Canada USA Harpoon
|
HRPN 10
|
|
Other LL by the other CPCs, and all other gears except HP
|
OTH 11
|
|
USA Survey
|
US Survey 12
|
|
EU Portugal Survey
|
PORT Survey 13
|
|
Age 1 Survey
|
Age 1
|
|
Age 2 Survey
|
Age 2
|
|
Age 3 Survey
|
Age 3
|
|
Age 4 Survey
|
Age 4
|
|
Age 5 plus Survey
|
Age 5 plus
|
Model Structure
The SS3 model used one season, one area, and two sexes.
Fixed Biological Parameters
Natural mortality for both male and female was fixed at 0.2 for all age classes. Maturity-at-age was knife-edge, with 50% at age-5 and 100% thereafter. Fecundity was proportional to body weight. The growth parameters for the 2022 assessment were fixed at same used in the 2017 assessment, which were developed during the 2017 ICCAT Swordfish Data Preparatory Meeting (See Anon., 2017 for details).
Selectivity
Selectivity was modeled as a function of length. Dome-shaped selectivity was allowed for five fleets: EU-Spain, USA, Japan, EU-Portugal, and Morocco. Asymptotic selectivity was assumed for Canada, Chinese-Taipei and Other. The age-specific survey CPUEs were modeled with a fixed age-based selectivity.
Retention and Discard Mortality
The 2022 assessment assumed a minimum legal length of 119 cm lower jaw fork length (LFFL) for all fleets from 1993 - 2020, and estimated the selectivity and retention curves from the available data. Discard mortality was either estimated from the observer data (USA and Canada) or fixed at values taken from the literature (see SCRS/2022/124).
Stock-Recruitment
Expected recruitment to age-0 was calculated from the total spawning stock biomass using the Beverton-Holt stock-recruit function. The standard error for the log-normally distributed recruitment deviations (sigmaR) was fixed to 0.2. Steepness (h) was fixed at the value estimated in the 2017 assessment (0.88).
Overview of Operating Model Conditioning
The 2022 stock assessment was used as the Base Case model for developing the operating models (OMs) evaluated in the MSE.
OM Uncertainty Grid
In 2019, the Swordfish Species Group developed an OM uncertainty grid using a full factorial design with 6 axes of uncertainty:
Natural mortality (M) - three levels: 0.1, 0.2, 0.3
Recruitment variability (sigmaR; \(\sigma_R\)) - two levels: 0.2, 0.6
Steepness (h) - three levels: 0.6, 0.75, 0.90
CPUE Lambda - three levels: 0.05, 1, 20
Directional trend in catchability (llq) - two levels: TRUE, FALSE
Environmental covariate (env) - two levels: TRUE, FALSE
The directional increase in catchability was modeled by assuming a 1% average annual increase in catchability throughout the history of the fishery, and adjusting the CPUE indices accordingly.
The environmental covariate was included by modeling catchability as a function of the AMO, as was done in the 2017 and 2022 stock assessments.
This factorial design of the 6 axes of uncertainty, each with 2 or 3 levels, resulted in an uncertainty grid of 216 OMs.
Evaluation of the OM Grid
Previous analyses of the OM uncertainty grid based on the 2017 assessment revealed that the three levels of natural mortality and steepness had the largest impact on the predicted stock dynamics Hordyk et al., 2021 and are therefore the most important axes of uncertainty in the OM grid.
The second level of recruitment variability (\(\sigma_R = 0.6\)) had a minor impact on the predicted stock dynamics Hordyk et al., 2021, but did influence the relative performance of candidate management procedures Hordyk, 2021. This second level is now treated as a robustness test (see below for more details).
The fourth axis of uncertainty was intended to evaluate the effect of alternative relative weightings of the length composition data and the indices of abundance. The three levels reflect a complete down-weighting of the indices of abundance (0.05; effectively only fitting the model to the length composition data), leaving the relative weighting of the two data sources unchanged from that used in the assessment (1), and up-weighting the indices of abundance so the model ignores the length composition data (20). This was done because of apparent conflicting signals between the length composition data and some of the indices of abundance, and the high computation demand of conducting the recommended iterative re-weighting procedure across all OMs in the grid (Francis, 2011). However, this iterative re-weighting procedure has now been conducted for the new operating models based on the 2022 assessment, and therefore this axis of uncertainty has been modified to two levels: 1) fit the assessment to both length and CPUE data and conduct the iterative re-weighting procedure, and 2) only fit the model to the CPUE data. This second level is now treated as a robustness test (see below for more details).
The fifth axis of uncertainty with the assumed 1% increase in catchability for the indices of abundance had a relatively minor influence on both the predicted stock dynamics Hordyk et al., 2021 and the performance of candidate management procedures Hordyk, 2021. Therefore it was decided that the default assumption for the operating models was to use the same indices as were used in the 2022 stock assessment; .e., the indices were not adjusted for an assumed 1% annual increase in changeability. The second level of this axis, where the indices are adjusted for an assumed increase in catchability, is now treated as a robustness test (see below for more details).
In both the 2017 and 2022 stock assessments, the catchability coefficient (q) for the CPUE indices for Canada, Japan, Portugal, Morocco, and the Spain age-specific survey indices, was made a function of the Atlantic Multidecadal Oscillation (AMO). Including this environmental covariate resulted in a better statistical fit to the data for these indices. The sixth axis of uncertainty examined the impact of not including this environmental covariate in the stock assessment. The analyses revealed that removing the environmental covariate from the assessment model had no detectable influence on either the predicted stock dynamics Hordyk et al., 2021 or the performance of candidate management procedures Hordyk, 2021. Therefore, the environmental covariate was included in all models in the OM grid. Further examination of the impact of changing environmental conditions on the performance of the candidate management procedures may be examined in additional robustness tests (see below for more details).
This same pattern in results was found when the analyses were re-conducted with the new OM grid based on the 2022 assessment.
Operating Models
The OMs, conditioned using SS3, were imported into MSE framework using the SS2MOM function in MSEtool (see here for details on the function).
Previously, the 2-sex SS3 models were imported into a combined single-sex operating model in the MSE framework. The MSE framework was updated in 2022, and the imported operating models now maintain the separate sexes in the same manner as the SS3 assessment. Since the fishery is managed with a single overall total allowable catch (TAC), the individual fishing fleets were aggregated together in the operating models.
The 2-sex operating model are available in the SWOMSE package as objects of class MOM (multi-stock operating model). For example, the 2022 stock assessment is available as the base case operating model named MOM_000:
library(SWOMSE)
MOM_000@Name
## [1] "M:0.2 sigmaR:0.2 steepness:0.88 Include CAL:TRUE llq:1 env:1 Class:Base Case"
The operating models have been classified into two categories: Reference OMs and Robustness OMs. Table 5.1 provides a general overview of the operating models includes in the MSE analysis. The following sub-sections describe the Reference and Robustness OMs in more detail.
Table 5.1: Summary of the Base Case, Reference, and Robustness Operating Models.
|
Class
|
Description
|
|
Base Case
|
The 2022 Stock Assessment, considered the Base Case OM
|
|
Reference
|
Span the key plausible uncertainties in natural mortality (M) and steepness of the Beverton-Holt stock-recruit relationship (h). Same as Base Case with 3 levels of M (0.1, 0.2, 0.3) and 3 levels of h (0.69, 0.80, 0.88)
|
|
R0
|
Reference OM for the Robustness Tests. MOM_005; M=0.2, h=0.8
|
|
R1
|
Evaluate impact of an assumed 1 percent annual increase catchability, that is not accounted for in the standardization of the indices of abundance (historical & projection)
|
|
R2
|
Same as R2, but only for the historical period
|
|
R3
|
Evaluate impact of increased recuitment variability in the projection period; a proxy for impact of climate change on productivity
|
|
R4
|
Evaluate impact of illegal, unreported, or unregulated catches
|
Base Case
The Base Case operating model is the 2022 Stock Assessment. See the Stock Assessment section for more details.
Reference Operating Models
The Reference OMs are based on the Base Case Model and span the plausible range for the key uncertainties. The Candidate Management Procedures (CMPs) are tuned across the Reference OMs (see the CMP Tuning section for more details.
Based on the analyses described above, a set of 9 operating models were identified as the Reference OMs. These operating models spanned the three levels of M (0.1, 0.2, and 0.3) and three levels h (0.69, 0.80, and 0.88).
The values for steepness in the Reference Set were determined at the 2023 Intersessional Meeting of the Swordfish Working Group. The lower and upper values were set at the 95% confidence intervals of a prior distribution for the steepness parameter used in the 2022 assessment.
The center value was calculated by first converting steepness to the Goodyear compensation ratio (CR) with \(\text{CR}=\frac{4h}{1-h}\). Using this equation, the CR for h = 0.69 and h = 0.88 are 8.90 and 29.33 respectively. A mid-point between these two CR values is calculated as:
\(\text{CR}_\text{mid} = e^{\frac{\sum_i\log\text{CR}_i}{2}}=16.16\), where the lower and upper values are 16.16/1.815 and 16.16 x 1.815 respectively. Back-calculating to steepness with \(h=\frac{\text{CR}}{\text{CR}+4}\) gives \(h=0.80\).
Initially the OMs with the lower steepness value \((h=0.6)\) was also considered to be in the Reference OMs. However, recent analysis and discussion with the MSE Technical Team has revealed that a such a low steepness is unlikely for North Atlantic swordfish. Consequently, the OMs with \(h=0.6\) have been re-classified into a Robustness Set.
The Reference OMs had the following assumptions:
- Following the assessment, \(\sigma_R = 0.2\)
- The Francis iterative re-weighting procedure was conducted on each operating model to find the appropriate weighting between the length compostion data and the indices of abundance
- The standardized indices of abundance were used (i.e., no assumed 1% increase in catchability)
R0. Reference Model
The robustness tests are conducted for a single operating model from center of the Reference Set (M=0.2, h=0.8). R0 describes the performance of the CMPs for this single operating model, and is used for evaluating the results of the other robustness tests.
R1. Hyperstable Indices in Historical and Projection Periods
The R1 Robustness test evaluates the impact of a consistent increase in catchability that is not accounted for in the standardization of the indices, resulting in a hyper-stable index.
This test is implemented by assuming that the indices used in the OM conditioning were ‘corrected’ for an average 1% annual increase in catchability. This was done by decreasing the observed values of the indices by 1% per year. Figure 5.1 shows the estimated F/FMSY and SB/SBMSY for the Reference model (R0) and the this robustness test (R1).
The MPs were provided with the observed historical indices (rather than the ‘corrected’ indices). The unaccounted 1% annual increase in catchability was assumed to continue in the projection period. This was done by assuming 1% annual positive observation error for the indices in the projection period (Figure 5.2).
R2. Hyperstable Indices in Historical Period
The R2 Robustness test is identical to R1 (Figure 5.1), except that the indices in the projection period are assumed to be proportional to the population; i.e., the 1% increase in catchability that was not accounted for the in the standardization of the historical indices no longer occurs in the projection period.
R3. Increased Recruitment Variability
The R3 Robustness test evaluates the impact of increased recruitment variability in the projection period. This is assumed to be a proxy for the potential impact of climate change on the productivity of the population, leading to a more dynamic stock in the future compared to the past.
This robustness test is implemented by increasing the recruitment variability in the projection years (Figure 5.3). This was done by generating values from a log-normal distribution with mean 0 and standard deviation of 0.2, and multiplying the recruitment deviations in the projection from the reference model (R0) by these values (Figure 5.3)
R4. TAC Implementation Error
The R4 Robustness test evaluates the impact of a 10% implementation error in the TAC. The catches are assumed to be 10% higher than the TAC, but these catches are assumed to be unreported (i.e., the observed catches provided to the CMPs is equal to the TAC and ~90% of the actual landings).
OM Validation
OM Summary Report
A Summary Report summarizes the diagnostic checks, the calculated biological reference points, and the estimated stock status relative to those reference points, across the Reference and Robustness operating models.
OM Diagnostic Reports
Individual diagnostic reports with objective function values and plots of model fits and patterns in residuals are available for each of the Reference and Robustness OMs (and the 2022 assessment) on the North Atlantic Swordfish MSE homepage.
Historical Spool-Up Period
The historical spool-up period was generated by importing the output from the SS3 assessment models and running the MSE simulation framework to recreate the historical fishery dynamics. No additional uncertainty was added to the historical simulations; that is, all \(50\) simulations were identical (e.g., same recruitment deviations) for the historical spool-up period.
The operating models have been conditioned on data up to and including 2020 (see the Data section), and therefore the projection period for the MSE framework starts in the following year, i.e., 2021. Currently, model uses assumed catches for the first three years (2021 - 2023) and then implements the management procedures in the following year (i.e., 2024). See the Future Catches section for more information.
Historical Data
The observed fishery data was imported into the MSE framework and made available to the CMPs. Three sources of data available to the CMPs: 1) catch data, 2) a primary index of abundance, and 3) additional individual indices.
Catch Data
The catch data used in the OM conditioning are made available in the simulated data that is provided to the CMPs (Figure 7.1).
Indices of Abundance
Primary Index
At the 2020 Swordfish MSE technical meeting (4 – 5 June 2020), the Group chose to use the Combined Index (Ortiz et al., 2017) as the primary index for the development of CMPs. This index was updated for the 2022 assessment. The Combined Index was imported into the SWOMSE framework and made available to the CMPs (Figure 7.2).
Other Indices
The additional individual fishery-dependent and survey indices are also imported into the MSE framework and made available to the CMPs (Figure 7.3).
Closed-Loop Simulation Testing
Simulation Specifications
The current simulation specifications for the MSE are:
- Management interval: 3 (i.e., TAC updated every 3rd year)
- Number of projection years: 33 (2021 – 2053)
- Number of simulations per OM: \(50\)
Assumptions
Recruitment
The stock-recruitment relationship in the projection years was modeled using the Beverton-Holt function, with steepness fixed at the value assumed in the OM conditioning.
Recruitment deviations for the projection period were generated assuming a log-normal distribution with mean \(\mu_R\) and variance \(\sigma_R^2\), calculated as:
\[\mu_R = -0.5\sigma_R^2\left(1-\frac{\text{AC}}{\sqrt{(1-\text{AC}^2)}}\right)\]
where AC is the lag-1 autocorrelation factor calculated from the historical recruitment deviations and \(\sigma_R^2\) is the variance of the recruitment deviations specified in the OM (i.e., sigmaR^2 defined above).
The recruitment deviations for the projection period were generated independently for each simulation.
Life-History
Biological parameters such as mean weight-at-age and maturity-at-age were constant for all years (historical and projection).
Selectivity
Currently, the selectivity-at-length for the projection years is fixed to the estimated selectivity pattern from the last historical year for each OM. The reviewer of the SWOMSE R code highlighted this as a possible uncertainty and recommended that the Group consider testing alternative assumptions for the selectivity pattern in the projection years (Anon., 2021).
The Working Group will discuss this assumption in more detail and decide if it wishes to run scenarios with alternative assumptions for the selectivity pattern in the projection years.
Catchability
No time-varying catchability is assumed for the projection years.
Future Catches
The total allowable catch (TAC) recommended by the CMPs is assumed to be implemented without error. That is, annual catches are equal to the TAC recommendations (provided sufficient vulnerable biomass was available). The Group determined that this assumption was appropriate based on the fact that historically the total landed catch has been below the total allowable catch (TAC) (see Figure 7.1).
A Robustness Test (R4) has been designed to evaluate the consequences of violating this assumption.
The operating models also assume that the TACs recommended by the CMPs apply to the landed catch and do not include discards; that is, the actual removals will be higher than the TACs if there is discard mortality on the sub-legal fish.
The CMPs are first implemented in 2024, and every 3rd year after that, with the TAC held constant in the interim years. Since the MSE projection period begins in 2021 (the year after the conditioning), the landed catches for the first few projection years until the CMPS are implemented (2021, 2022, and 2023) are fixed and not set by the CMPs. The catch for 2021 is set at the reported level, and the catches for 2022 and 2023 are set to the mean from previous 10 years (2012 – 2021) (Table 8.1).
Table 8.1: The assumed landed catch (ton) for the first 3 projection years.
|
Year
|
Catch
|
Details
|
|
2021
|
9729
|
Reported Catch
|
|
2022
|
10770
|
Assumed Catch
|
|
2023
|
10770
|
Assumed Catch
|
Generation of Future Data
Indices
The primary index of abundance (Combined Index) was generated in the projection years by adding observation error to the projected stock biomass, and standardizing to be on the same scale as the historical observed Combined Index.
The additional individual indices were generated in the projection years in the same manner, using the simulated stock biomass (or abundance in some cases).
The observation error was generated by calculating the statistical properties of the residuals from the fit of each respective index to the simulated stock (biomass or abundance) during the historical period. The procedure for calculating the residuals for the projection years is described in detail in the openMSE documentation.
It was decided at the 2023 Intersessional Meeting of the Swordfish Working Group to calculate the statistical properties of the observation error for the Combined Index for the years 1999 – 2020. This period was chosen as it includes only the years where the data used to generate the index included the majority of the fleets.
Following the assumptions of the stock assessment, it is assumed that all indices are proportional to the respective stock biomass or abundance (i.e., it is assumed that the indices are not hyper-stable or hyper-deplete). The only exception to this is Robustness Set 5 where the indices are assumed to have a 1% annual average increase in catchability.
Catches
Following the assumption of the stock assessment, the observed catches in the projection years were generated with no observation error. That is, the observed catches in the MSE framework are equal to the actual simulated landed catches (provided there is sufficient biomass in the simulated population to support the prescribed TAC). The model does not include a bio-economic component and inter-annual change in fishing effort is unconstrained.
Data Lags
In the MSE framework, the CMPs are always provided with the simulated fishery data up to the previous year. That is, if a CMP is implemented in year \(t\), the catch and indices of abundance data provided to the CMP are up to and including year \(t-1\).
Data lags are then handled in the CMP code, where the default assumption is that the catch and index data are lagged by 2 years from the year where the TAC will be implemented; e.g., for setting the TAC for 2024, the data is up to and including 2022. See the CMP (see the CMP Development Guide for more details).
Candidate Management Procedures
Tuning of Candidate Management Procedures
During development, the Candidate Management Procedures are tuned to achieve specific performance outcomes. These performance outcomes are calculated across all the Reference OMs; i.e, the MSE results from each Reference OM are aggregated together. 3 different tuning targets have been specified (Table 10.1).
There will be 3 versions of each CMP corresponding to metrics and targets described in Table 10.1. The names of the CMPs include the tuning code (i.e., ‘a’, ‘b’).
Table 10.1: The codes, metrics, and targets used for the tuning of the CMPs.
|
Code
|
Metric
|
Target
|
|
a
|
PGK_short
|
0.51
|
|
b
|
PGK_short
|
0.60
|
|
c
|
PGK_short
|
0.70
|
Reporting of MSE Results
Summary Tables
Summary Plots
Interactive Application
TODO - describe Slick
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ICCAT Atlantic Swordfish Stock Assessment Session.
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Anon. (2021). Peer review of the North Atlantic Swordfish Management Strategy Evaluation (MSE) code and algorithms SCRS/2021/097. Collect. Vol. Sci. Pap. ICCAT, 78(7).
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Canadian Journal of Fisheries and Aquatic Sciences,
68(6), 1124–1138.
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A biological and statistical framework for fish stock assessment and fishery management.
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Ortiz, M., Mejuto, J., Hanke, A., Ijima, H., Walter, J., Coelho, R., & Ikkiss, A. (2017). Updated combined biomass index of abundance of
North Atlantic swordfish stock 1963 - 2015.
SCRS/2017/137.
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